Copy Number Arrays
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ABSTRACT:
PROVIDER: EGAD00010001581 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Copy Number Alterations arrays from 153 samples performed by Affymetrix 6.0 and Oncoscan CNV platforms
| EGAD00010001841 | EGA
Project description:Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive malignancy assumed to originate from plasmacytoid dendritic cells (pDCs), which affects the skin and bone marrow and sequentially other organ systems. Here, we used Affymetrix OncoScan CNV arrays to characterize somatic copy number variations in 45 BPDCN cases.
2022-04-05 | GSE200113 | GEO
Project description:Affymetrix Oncoscan arrays were performed according to the manufacturer's directions on DNA extracted from FFPE-breast cancer tissues.
2016-07-01 | GSE83916 | GEO
Project description:Affymetrix Oncoscan arrays were performed according to the manufacturer's directions on DNA extracted from FFPE-breast cancer tissues. Copy number analysis using Affymetrix Oncoscan arrays was performed for 29 primary breast cancers
2016-07-01 | E-GEOD-83916 | biostudies-arrayexpress
Project description:Background: High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results: The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4 - 489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0 - 86 %). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions: High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies. NA12878 hybridization to Affymetrix CytoScan HD array for CNV detection
2017-03-23 | GSE96897 | GEO
Project description:Copy number variations (CNVs) constitute the largest portion of the human genome variation. We determined a genome-wide high resolution SNP/CNV haplotype structure of Asians, by analyzing a collection of complete hydatidiform moles (CHMs) of Japanese, using high-density DNA arrays. CHMs are tissues carrying duplicated haploid genomes derived from single sperms, and are suitable material for the detection of CNVs, because they are expected to reveal greater signal to noise ratio in hybridization experiments. Also, the absence of heterozygosity ensures straightforward CNV interpretation without being bothered by overlapping CNV segments. We genotyped 100 CHM genomes using Affymetrix SNP 6.0 and Illumina 1M-duo, created a definitive haplotype map including 1.7 million SNPs and 2339 CNV region (CNVR) that is presented as D-HaploDB Phase 4.1.
2014-02-13 | GSE54948 | GEO
Project description:Affymetrix Oncoscan assays were performed according to the manufacturer's instructions from DNA extracted from FFPE tissues. Copy number analysis using Affymetrix Oncoscan arrays was performed for 16 APMF Samples.
2020-04-11 | E-MTAB-7888 | biostudies-arrayexpress
Project description:Affymetrix OncoScan SNP arrays for profiling somatic copy-number alterations in blastic plasmacytoid dendritic cell neoplasm
| PRJNA823007 | ENA
Project description:Background High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4 - 489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0 - 86 %). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.
2017-03-20 | GSE96791 | GEO
Project description:Background High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. Results The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4 - 489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0 - 86 %). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. Conclusions High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.
2017-03-20 | GSE96790 | GEO